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How a DRG Determines How Much a Hospital Gets Paid

Medicare and certain private health insurance companies pay for hospitalizations of their beneficiaries using a diagnosis-related group (DRG) payment system . This article will explain how the DRG system works, and how it determines the payment amounts that hospitals receive.

When you've been admitted as an inpatient to a hospital, that hospital assigns a DRG when you're discharged, basing it on the diagnosis you received and the treatment that you needed during your hospital stay. The hospital gets paid a fixed amount for that DRG, regardless of how much money it spent treating you.

If a hospital can effectively treat you for less money than Medicare pays for your DRG, then the hospital makes money on that hospitalization. If the hospital spends more money caring for you than Medicare gives it for your DRG, then the hospital loses money on that hospitalization.

What Does DRG Mean?

DRG stands for diagnosis-related group. Medicare's DRG system is called the Medicare severity diagnosis-related group, or MS-DRG, which is used to determine hospital payments under the inpatient prospective payment system (IPPS). It's the system used to classify various diagnoses for inpatient hospital stays into groups and subgroups so that Medicare can accurately pay the hospital bill.

The idea behind DRGs is to ensure that Medicare reimbursements adequately reflect " the fundamental role which a hospital’s case mix (the type of patients the hospitals treats, and the severity of their medical issues) plays in determining its costs " and the number of resources that the hospital needs to treat its patients.

The diagnoses that are used to determine the DRG are based on ICD-11 codes or ICD-10 codes (the ICD-11 codes went into effect in 2022, but some areas are still using ICD-10 codes). Additional codes were added to that system in 2021 and 2022, to account for the COVID-19 pandemic, and a 20% MS-DRG add-on payment was added during the pandemic when hospitals treated COVID-19 patients.

DRGs have historically been used for inpatient care, but the 21st Century Cures Act, enacted in late 2016, required the Centers for Medicare and Medicaid Services to develop some DRGs that apply to outpatient surgeries. These are required to be as similar as possible to the DRGs that would apply to the same surgery performed on an inpatient basis.

Medicare and private insurers have also piloted new payment systems that are similar to the current DRG system, but with some key differences, including an approach that combines inpatient and outpatient services into one payment bundle. In general, the idea is that bundled payments are more efficient and result in better patient outcomes than fee-for-service payments (with the provider being paid based on each service that's performed).

Figuring Out How Much Money a Hospital Gets Paid for a Given DRG

In order to figure out how much a hospital gets paid for any particular hospitalization, you must first know what DRG was assigned for that hospitalization. In addition, you must know the hospital’s base payment rate, which is also described as the "payment rate per case." You can call the hospital’s billing, accounting, or case management department and ask what its Medicare base payment rate is.

Each DRG is assigned a relative weight based on the average amount of resources it takes to care for a patient assigned to that DRG. You can look up the relative weight for your particular DRG by downloading a chart provided by the Centers for Medicare and Medicaid Services following these instructions:

• Go to the CMS payment systems webpage .
• Scroll down to "FY 2024 Final Rule and Correcting Amendment Tables" (note that this is for Fiscal Year 2024)
• Download Table 5 ("MS-DRGs, Relative Weighting Factors and Geometric and Arithmetic Mean Length of Stay").
• Open the file that displays the information as an Excel spreadsheet (the file that ends with “.xlsx”).
• The column labeled “weights” shows the relative weight for each DRG.

The average relative weight is 1.0. DRGs with a relative weight of less than 1.0 are less resource-intensive to treat and are generally less costly to treat. DRGs with a relative weight of more than 1.0 generally require more resources to treat and are more expensive to treat. The higher the relative weight, the more resources are required to treat a patient with that DRG. This is why very serious medical situations, such as organ transplants , have among the highest DRG weight.

To figure out how much money your hospital got paid for your hospitalization, multiply your DRG’s relative weight by your hospital’s base payment rate.

Here’s an example with a hospital that has a base payment rate of $6,000 when your DRG’s relative weight is 1.3:

$6,000 X 1.3 = $7,800. Your hospital got paid $7,800 for your hospitalization.

How a Hospital’s Base Payment Rate Works

The base payment rate is broken down into a labor portion and a non-labor portion. The labor portion is adjusted in each area based on the wage index. The non-labor portion varies for Alaska and Hawaii, according to a cost-of-living adjustment.

Since healthcare resource costs and labor vary across the country and even from hospital to hospital, Medicare assigns a different base payment rate to each and every hospital that accepts Medicare.

For example, a hospital in Manhattan, New York City probably has higher labor costs, higher costs to maintain its facility, and higher resource costs than a hospital in Knoxville, Tennessee. The Manhattan hospital probably has a higher base payment rate than the Knoxville hospital.

Other things that Medicare factors into your hospital’s blended rate determination include whether or not it’s a teaching hospital with residents and interns, whether or not it’s in a rural area, and whether or not it cares for a disproportionate share of the poor and uninsured population. Each of these things tends to increase a hospital’s base payment rate.

Each October, Medicare assigns every hospital a new base payment rate. In this way, Medicare can tweak how much it pays any given hospital, based not just on nationwide trends like inflation, but also on regional trends. For example, as a geographic area becomes more developed, a hospital within that area may lose its rural designation.

In 2020, the Centers for Medicare and Medicaid Services approved 24 new technologies that are eligible for add-on payments, in addition to the amount determined based on the DRG.

Are Hospitals Making or Losing Money?

After the MS-DRG system was implemented in 2008, Medicare determined that hospital-based payment rates had increased by 5.4% as a result of improved coding (i.e., not as a result of anything having to do with the severity of patients' medical issues).

So Medicare reduced the base payments rates to account for this. But hospital groups contend that the increase due to improved coding was actually only 3.5% and that their base rates had been reduced by too much with an expected $41.3 billion loss in hospital revenue from 2013 to 2028.

Hospitals in rural areas are especially struggling. More than 150 rural hospitals closed from 2005 to 2019, another 18 hospitals in 2020, and 19 hospitals from 2021 to 2023, nine of them in 2023. The Center for Healthcare Quality and Payment Reform reported in 2023 that as many as a third of rural hospitals, more than 600 facilities, remain at risk of closing in the near future.

Rural hospitals are not the only ones at risk. The pandemic triggered a workforce shortage in the healthcare industry and hospitals across the board had to pay more for contract labor and staffing to fill in those gaps. Rising rates of inflation have also increased non-labor expenses, i.e., the cost of drugs, medical equipment and supplies, building maintenance, sanitation, information technology and cybersecurity, and even food. Altogether, the American Hospital Association estimates these factors increased hospital spending to the point that more than half of hospitals had negative margins at the end of 2022.

The challenge is how to ensure that some hospitals aren't operating in the red under the same payment systems that put other hospitals well into the profitable realm. That's a complex task, though, involving more than just DRG-based payment systems, and it promises to continue to be a challenge for the foreseeable future.

When a patient with Medicare (or many types of private insurance) is hospitalized, a diagnostic related category (DRG) code is assigned based on the patient's condition. There are numerous factors that go into determining the DRG for each patient, and each DRG has a different relative weight, depending on the resources that are generally needed to provide care for someone with that DRG.

Each hospital also has a blended base rate, which is based on a variety of factors, including location, patient demographics, whether it's a teaching hospital, etc. The relative weight of the DRG is multiplied by the hospital's base rate to determine how much the hospital will be paid for that patient.

A Word From Verywell

Although there's a complex formula that determines how much a hospital gets paid for each patient, you don't have to know the details of exactly how it works. From a patient perspective, the most important details are ensuring that the hospital is in-network with your health plan, and understanding how your health plan's cost-sharing works.

An inpatient stay will generally result in having to pay your deductible, and maybe meeting your plan's annual out-of-pocket cap. You'll want to understand how much those expenses are, so that you're not caught off guard when the bills arrive.

Research Data Assistance Center. International Classification of Disease (ICD) Codes in Medicare Files .

Centers for Medicare and Medicaid Services. 2022 ICD-10-CM. COVID-19 Update.

National Institutes of Health. Changes in US Hospital Financial Performance During the COVID-19 Public Health Emergency . July 2023.

Congress.gov. H.R.34 - 114th Congress (2015-2016): 21st Century Cures Act .

Centers for Medicare and Medicaid Services. MS-DRG Classifications and Software .

Centers for Medicare and Medicaid Services. Bundled Payments for Care Improvement (BPCI) Initiative .

Centers for Medicare and Medicaid Services. Acute Inpatient PPS .

Centers for Medicare and Medicaid Services. Fiscal Year (FY) 2021 Medicare Hospital Inpatient Prospective Payment System (IPPS) and Long Term Acute Care Hospital (LTCH) Final Rule (CMS-1735-F) . September 2, 2020.

Dobson DaVanzo & Associates, LLC. Estimate of Federal Payment Reductions to Hospitals Following the ACA 2010-2028. Estimates and Methodology. American Hospital Association .

Center for Healthcare Quality and Payment Reform (2023). Hundreds of Rural Hospitals Were at Immediate Risk of Closure before the Pandemic Hundreds More Rural Hospitals Are at High Risk of Closing in the Future .

American Hospital Association. Costs of Caring .

Federal Register, Medicare Program; Hospital Inpatient Prospective Payment Systems for Acute Care Hospitals and the Long-Term Care Hospital Prospective Payment System Policy Changes and Fiscal Year 2016 Rates; Revisions of Quality Reporting Requirements for Specific Providers, Including Changes Related to the Electronic Health Record Incentive Program; Extensions of the Medicare-Dependent, Small Rural Hospital Program and the Low-Volume Payment Adjustment for Hospitals, 8/17/15. 

By Elizabeth Davis, RN Elizabeth Davis, RN, is a health insurance expert and patient liaison. She's held board certifications in emergency nursing and infusion nursing.

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Statistical methods for assigning weights based on rank differences?

I have encountered methodological problem in my pursuit of my master's degree, and I hope you can help! I know exactly what I want to do, but I do not know which statistical area this is related to. I have created the following picture depiction, of what my goal is:

From the picture above, each person has a feature attached to him/her. Either one of these three features, is a composite measure based on several variables. If person 0 is the "base/target" person, and I want to calculate how different each subjects are, I calculate the absolute rank difference.

Based on these rank differences, it becomes evident that person 3 is closest (in terms of features) to person 0. Now, what I want to know from this - is how to assign weights based on these rank differences. The goal is to take the known people (person 1, 2, 3 and 4) and estimate a Z value for person 0, based on some weighting procedure of the rank differences (or maybe the "strength/importance" of each feature?).

Do you have any recommendations, as to what methods I can look into? Furthermore, if I want to see which variables in the composite scores, that are most important for making the features x1, x2 and x3, what could I do? I was thinking of a regression, but there might be issues with multicollinearity.

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What Is Weighted Average?

Weighting a stock portfolio.

• Pros and Cons
• Types of Averages

The Bottom Line

• Corporate Finance
• Financial statements: Balance, income, cash flow, and equity

Weighted Average: Definition and How It Is Calculated and Used

A weighted average is a calculation that takes into account the varying degrees of importance of the numbers in a data set. A weighted average can be more accurate than a simple average in which all numbers in a data set are assigned an identical weight.

Key Takeaways

• The weighted average takes into account the relative importance or frequency of some factors in a data set.
• A weighted average is sometimes more accurate than a simple average.
• In a weighted average, each data point value is multiplied by the assigned weight, which is then summed and divided by the number of data points.
• A weighted average can improve the data’s accuracy.
• Stock investors use a weighted average to track the cost basis of shares bought at varying times.

Weighted Average

Investopedia / Paige McLaughlin

What Is the Purpose of a Weighted Average?

In calculating a simple average, or arithmetic mean , all numbers are treated equally and assigned equal weight. But a weighted average assigns weights that determine in advance the relative importance of each data point. In calculating a weighted average, each number in the data set is multiplied by a predetermined weight before the final calculation is made.

A weighted average is most often computed to equalize the frequency of the values in a data set. For example, a survey may gather enough responses from every age group to be considered statistically valid, but the 18 to 34 age group may have fewer respondents than all others relative to their share of the population . The survey team may weigh the results of the 18 to 34 age group so that their views are represented proportionately.

However, values in a data set may be weighted for other reasons than the frequency of occurrence. For example, if students in a dance class are graded on skill, attendance, and manners, the grade for skill may be given greater weight than the other factors.

Each data point value in a weighted average is multiplied by the assigned weight, which is then summed and divided by the number of data points. The final average number reflects the relative importance of each observation and is thus more descriptive than a simple average. It also has the effect of smoothing out the data and enhancing its accuracy.

Investors usually build a position in a stock over a period of several years. That makes it tough to keep track of the cost basis on those shares and their relative changes in value. The investor can calculate a weighted average of the share price paid for the shares. To do so, multiply the number of shares acquired at each price by that price, add those values, then divide the total value by the total number of shares.

A weighted average is arrived at by determining in advance the relative importance of each data point.

For example, say an investor acquires 100 shares of a company in year one at $10, and 50 shares of the same stock in year two at $40. To get a weighted average of the price paid, the investor multiplies 100 shares by $10 for year one and 50 shares by $40 for year two, then adds the results to get a total of $3,000. Then the total amount paid for the shares, $3,000 in this case, is divided by the number of shares acquired over both years, 150, to get the weighted average price paid of $20.

This average is now weighted with respect to the number of shares acquired at each price, not just the absolute price.

The weighted average is sometimes also called the weighted mean.

Advantages and Disadvantages of Weighted Average

Pros of weighted average.

Weighted average provides a more accurate representation of data when different values within a dataset hold varying degrees of importance. By assigning weights to each value based on their significance, weighted averages ensure that more weight is given to data points that have a greater impact on the overall result. This allows for a more nuanced analysis and decision-making process.

Next, weighted averages are particularly useful for handling skewed distributions or outliers within a dataset. Instead of being overly influenced by extreme values, weighted averages take into account the relative importance of each data point. This means you can "manipulate" your data set so it's more relevant, especially when you don't want to consider extreme values.

Thirdly, weighted averages offer flexibility in their application across various fields and disciplines. Whether in finance, statistics, engineering, or manufacturing , weighted averages can be customized to suit specific needs and objectives. For instance, like we discussed above, weighted averages are commonly used to calculate portfolio returns where the weights represent the allocation of assets. Weighted averages can also be used in the manufacturing process to determine the right combination of goods to use.

Cons of Weighted Average

One downside of a weighted average is the potential for subjectivity in determining the weights assigned to each data point. Deciding on the appropriate weights can be challenging, and it often involves subjective judgment where you don't actually know the weight to attribute. This subjectivity can introduce bias into the analysis and undermine the reliability of the weighted average.

Weighted averages may be sensitive to changes in the underlying data or weighting scheme. Small variations in the weights or input values can lead to significant fluctuations in the calculated average, making the results less stable and harder to interpret. This sensitivity can be particularly problematic in scenarios where the weights are based on uncertain or volatile factors which may include human emotion (i.e. are you confident you'll feel the same about the appropriate weights over time?).

Last, the interpretation of weighted averages can be more complex compared to simple arithmetic means. Though weighted averages provide a single summary statistic, they may make it tough to understand the full scope of the relationship across data points. Therefore, it's essential to carefully assess how the weights are assigned and the values are clearly communicated to those who interpret the results.

Accurate representation via weighted significance, aiding nuanced decision-making.

Handles outliers, mitigating extreme value influence for relevance.

Flexible across fields, tailor needs, or objectives.

Subjectivity in determining weights introduces bias and undermines reliability.

Sensitivity to changes in data or weighting scheme affects stability.

Adds complexity compared to arithmetic mean, potentially obscuring analysis

Examples of Weighted Averages

Weighted averages show up in many areas of finance besides the purchase price of shares, including portfolio returns , inventory accounting, and valuation. When a fund that holds multiple securities is up 10% on the year, that 10% represents a weighted average of returns for the fund with respect to the value of each position in the fund.

For inventory accounting, the weighted average value of inventory accounts for fluctuations in commodity prices, for example, while LIFO (last in, first out) or FIFO (first in, first out) methods give more importance to time than value.

When evaluating companies to discern whether their shares are correctly priced, investors use the weighted average cost of capital (WACC) to discount a company’s cash flows. WACC is weighted based on the market value of debt and equity in a company’s capital structure.

Weighted Average vs. Arithmetic vs. Geometric

Weighted averages provide a tailored solution for scenarios where certain data points hold more significance than others. However, there are other forms of calculating averages, some of which were mentioned earlier. The two main alternatives are the arithmetic average and geometric average.

Arithmetic means, or simple averages, are the simplest form of averaging and are widely used for their ease of calculation and interpretation. They assume that all data points are of equal importance and are suitable for symmetrical distributions without significant outliers. Arithmetic means will often be easier to calculate since you divide the sum of the total by the number of instances. However, it is much less nuanced and does not allow for much flexibility.

Another common type of central tendency measure is the geometric mean . The geometric mean offers a specialized solution for scenarios involving exponential growth or decline. By taking the nth root of the product of n values, geometric means give equal weight to the relative percentage changes between values. This makes them particularly useful in finance for calculating compound interest rates or in epidemiology for analyzing disease spread rates.

A weighted average is a statistical measure that assigns different weights to individual data points based on their relative significance, resulting in a more accurate representation of the overall data set. It is calculated by multiplying each data point by its corresponding weight, summing the products, and dividing by the sum of the weights.

Is Weighted Average Better?

Whether a weighted average is better depends on the specific context and the objectives of your analysis. Weighted averages are better when different data points have varying degrees of importance, allowing you to have a more nuanced representation of the data. However, they may introduce subjectivity in determining weights and can be sensitive to changes in the weighting scheme

How Does a Weighted Average Differ From a Simple Average?

A weighted average accounts for the relative contribution, or weight, of the things being averaged, while a simple average does not. Therefore, it gives more value to those items in the average that occur relatively more.

What Are Some Examples of Weighted Averages Used in Finance?

Many weighted averages are found in finance, including the volume-weighted average price (VWAP) , the weighted average cost of capital, and exponential moving averages (EMAs) used in charting. Construction of portfolio weights and the LIFO and FIFO inventory methods also make use of weighted averages.

How Do You Calculate a Weighted Average?

You can compute a weighted average by multiplying its relative proportion or percentage by its value in sequence and adding those sums together. Thus, if a portfolio is made up of 55% stocks, 40% bonds, and 5% cash, those weights would be multiplied by their annual performance to get a weighted average return. So if stocks, bonds, and cash returned 10%, 5%, and 2%, respectively, the weighted average return would be (55 × 10%) + (40 × 5%) + (5 × 2%) = 7.6%.

Statistical measures can be a very important way to help you in your investment journey. You can use weighted averages to help determine the average price of shares as well as the returns of your portfolio. It is generally more accurate than a simple average. You can calculate the weighted average by multiplying each number in the data set by its weight, then adding up each of the results together.

Tax Foundation. “ Inventory Valuation in Europe .”

My Accounting Course. “ Weighted Average Cost of Capital (WACC) Guide .”

CDC. " Measures of Spread ."

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Relative Weight Analysis

This post includes detailed explanation of Relative Weight Analysis (RWA) along with its implementation in statistical softwares and programming like R, Python, SPSS and SAS.

RWA is quite popular in survey analytics world, mainly used to perform driver/impact analysis. For example which human resource driver makes employees stay or leave the organisation. Is 'pay' driver important than 'work-life balance'?. It is also called Relative Importance Analysis.

Relative Weight Analysis is a useful technique to calculate the relative importance of predictors (independent variables) when independent variables are correlated to each other. It is an alternative to multiple regression technique and it addresses multicollinearity problem and also helps to calculate the importance rank of variables. It helps to answer "Which variable is the most important and rank variables based on their contribution to R-Square".

When independent variables are correlated, it is difficult to determine the correct prediction power of each variable. Hence, it is difficult to rank them as we are unable to estimate coefficients correctly. Statistically, multicollinearity can increase the standard error of the coefficient estimates and make the estimates very sensitive to minor changes in the model. It means the coefficients are biased and difficult to interpret.

It creates a set of new independent variables that are the maximally related to the original independent variables but are uncorrelated to each other. Because these new transformed independent variables are uncorrelated to each other, the dependent variable can be regressed onto this new set of independent variables producing a series of standardized regression coefficients.

How to calculate Relative Weight Analysis?

Below are the steps to calculate Relative Weight Analysis (RWA)

• Compute correlation matrix between independent variables
• Calculate Eigenvector and Eigenvalues on the above correlation matrix
• Calculate diagonal matrix of eigenvalue and then take square root of the diagonal matrix
• Calculate matrix multiplication of eigenvector, matrix in step 3 and Transpose of Eigenvector
• Square the above matrix
• To calculate the partial effect of each independent variable on dependent variable, calculate matrix multiplication of [Inverse of matrix in step 4] and correlation matrix [between dependent and independent variables (i.e. 1 X 1 matrix)]
• To calculate R-Square, sum the above matrix (Step 6 matrix)
• To calculate raw relative weights, calculate matrix multiplication of [matrix in step 5] and [Square of matrix in step 6]
• To calculate raw relative weights as percentage of R-Square, divide raw relative weights by r-square and then multiply it by 100.

In the next section I have included programs to run RWA Analysis. Before running the analysis, it is important to ensure you don't have missing values in both independent and dependent variables. If you have missing values, it is important to impute or remove them. Also ensure you provide only numeric values in the target and predictors arguments in the programs below.

Calculate Relative Weight Analysis with Python, R, SAS and SPSS

Python Code

Signs of Beta can be interpreted as if predictor variable is positively or negatively impacting target variable. Negative sign denotes negative relationhip, positive sign denotes positive relationship.

Deepanshu founded ListenData with a simple objective - Make analytics easy to understand and follow. He has over 10 years of experience in data science. During his tenure, he worked with global clients in various domains like Banking, Insurance, Private Equity, Telecom and HR.

Thanks for this interesting post. However I'm experiencing some difficulties in following some steps, particularly steps 4 and 6 Could you please post an example of processing, say a 3 x 3 correlation matrix ? Thanks in advance

This is excellent!

KlaZe it 13roSki

Thanks a lot. This is very helpful. May I know how to revise the syntax if the raw data is a correlation matrix?

Thank you for the code. Worked really well. Do you have a reference on interpretation of output?

Where did you run it? i used spss 20 and produced errors

Thanks for the above, I tried to use the syntax on spss 20 but produces error. Any advice?

How I can calculate RII for multiple variables by using SPSS means for clients, contractor consultant and other factors and there are many questions under above listed parties

Optimal representative sample weighting

• Published: 28 February 2021
• Volume 31 , article number  19 , ( 2021 )

Cite this article

• Shane Barratt   ORCID: orcid.org/0000-0002-7127-0724 1 ,
• Guillermo Angeris 1 &
• Stephen Boyd 1  

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We consider the problem of assigning weights to a set of samples or data records, with the goal of achieving a representative weighting, which happens when certain sample averages of the data are close to prescribed values. We frame the problem of finding representative sample weights as an optimization problem, which in many cases is convex and can be efficiently solved. Our formulation includes as a special case the selection of a fixed number of the samples, with equal weights, i.e., the problem of selecting a smaller representative subset of the samples. While this problem is combinatorial and not convex, heuristic methods based on convex optimization seem to perform very well. We describe our open-source implementation rsw and apply it to a skewed sample of the CDC BRFSS dataset.

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All data and material are freely available online at www.github.com/cvxgrp/rsw .

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Acknowledgements

The authors would like to thank Trevor Hastie, Timothy Preston, Jeffrey Barratt, and Giana Teresi for discussions about the ideas described in this paper.

Shane Barratt is supported by the National Science Foundation Graduate Research Fellowship under Grant No. DGE-1656518.

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A Iterative proportional fitting

The connection between iterative proportional fitting, initially proposed by Deming and Stephan ( 1940 ) and the maximum entropy weighting problem has long been known and has been explored by many authors Teh and Welling ( 2003 ), Fu et al. ( 2019 ), She and Tang ( 2019 ), Wittenberg ( 2010 ), Bishop et al. ( 2007 ). We provide a similar presentation to She and Tang ( 2019 ), Sect. 2.1, though we show that the iterative proportional fitting algorithm that is commonly implemented is actually a block coordinate descent algorithm on the dual variables, rather than a direct coordinate descent algorithm. Writing this update in terms of the primal variables gives exactly the usual iterative proportional fitting update over the marginal distribution of each property.

Maximum entropy problem In particular, we will analyze the application of block coordinate descent on the dual of the problem

with variable \(w \in {\mathbf{R}}^n\) , where the problem data matrix is Boolean, i.e., \(F \in \{0, 1\}^{m \times n}\) . This is just the maximum entropy weighting problem given in Sect.  3.1 , but in the specific case where F is a matrix with Boolean entries.

Selector matrices We will assume that we have several possible categories \(k=1, \dots , N\) which the user has stratified over, and we will define selector matrices \(S_k \in \{0,1\}^{p_k \times m}\) which ‘pick out’ the rows of F containing the properties for property k . For example, if the first three rows of F specify the data entries corresponding to the first property, then \(S_1\) would be a matrix such that

and each column of \(S_1F\) is a unit vector, i.e., a vector whose entries are all zeros except at a single entry, where it is one. This is the same as saying that, for some property k , each data point is allowed to be in exactly one of the \(p_k\) possible classes. Additionally, since this should be a proper probability distribution, we will also require that \({\mathbf {1}}^TS_k f^\mathrm {des} = 1\) , i.e., the desired marginal distribution for property k should itself sum to 1.

Dual problem To show that iterative proportional fitting is equivalent to block coordinate ascent, we first formulate the dual problem (Boyd and Vandenberghe 2004 , Ch. 5). The Lagrangian of ( 6 ) is

where \(\nu \in {\mathbf{R}}^n\) is the dual variable for the first constraint and \(\lambda \in {\mathbf{R}}\) is the dual variable for the normalization constraint. Note that we do not need to include the nonnegativity constraint on \(w_i\) , since the domain of \(w_i \log w_i\) is \(w_i \ge 0\) .

The dual function (Boyd and Vandenberghe 2004 , Sect. 5.1.2) is given by

which is easily computed using the Fenchel conjugate of the negative entropy (Boyd and Vandenberghe 2004 , Sect. 3.3.1):

where \(\exp \) of a vector is interpreted componentwise. Note that the optimal weights \(w^\star \) are exactly those given by

Strong duality Because of strong duality, the maximum of the dual function ( 7 ) has the same value as the optimal value of the original problem ( 6 ) (Boyd and Vandenberghe 2004 , Sect. 5.2.3). Because of this, it suffices to find an optimal pair of dual variables, \(\lambda \) and \(\nu \) , which can then be used to find an optimal \(w^\star \) , via ( 9 ).

To do this, first partially maximize g with respect to \(\lambda \) , i.e.,

We can find the minimum by differentiating ( 8 ) with respect to \(\lambda \) and setting the result to zero. This gives

This also implies that, after using the optimal \(\lambda ^\star \) in ( 9 ),

Block coordinate ascent In order to maximize the dual function \(g^p\) , we will use the simple method of block coordinate ascent with respect to the dual variables corresponding to the constraints of each of the possible k categories. Equivalently, we will consider updates of the form

where \(\nu ^t\) is the dual variable at iteration t , while \(\xi ^t \in {\mathbf{R}}^{p_t}\) is the optimization variable we consider at iteration t . To simplify notation, we have used \(S_t\) to refer to the selector matrix at iteration t , if \(t \le N\) , and otherwise set \(S_t = S_{(t-1 \mod N) + 1}\) , i.e., we choose the selector matrices in a round robin fashion. The updates result in an ascent algorithm, which is guaranteed to converge to the global optimum since \(g^p\) is a smooth concave function Tseng ( 2001 ).

Block coordinate update In order to apply the update rule to \(g^p(\nu )\) , we first work out the optimal steps defined as

To do this, set the gradient of \(g_p\) to zero,

which implies that

where \(f_i\) is the i th column of F and the division is understood to be elementwise.

To simplify this expression, note that, for any unit basis vector \(x \in {\mathbf{R}}^m\) (i.e., \(x_i = 1\) for some i and 0, otherwise), we have the simple equality,

where \(\circ \) indicates the elementwise product of two vectors. Using this result with \(x = S_t f_i\) on each term of the numerator from the left hand side of ( 11 ) gives

where \(y = \sum _{i=1}^m \exp (-f_i^T\nu ^t)Sf_i\) . We can then rewrite ( 11 ) in terms of y by multiplying the denominator on both sides of the expression:

Since \({\mathbf {1}}^TS_t f^\mathrm {des} = 1\) ,

or, after solving for \(\xi \) ,

where the logarithm is taken elementwise. The resulting block coordinate ascent update can be written as

where the logarithm and division are performed elementwise. This update can be interpreted as changing \(\nu \) in the entries corresponding to the constraints given by property t by adding the log difference between the desired distribution and the (unnormalized) marginal distribution for this property suggested by the previous update. This follows from ( 10 ), which implies \(w_i^t \propto \exp (-f_i^T\nu ^t)\) for each \(i=1, \dots , n\) , where \(w^t\) is the distribution suggested by \(\nu ^t\) at iteration t .

Resulting update over w We can rewrite the update for the dual variables \(\nu \) as a multiplicative update for the primal variable w , which is exactly the update given by the iterative proportional fitting algorithm. More specifically, from ( 10 ),

For notational convenience, we will write \(x_{t i}= S_t f_i\) , which is a unit vector denoting the category to which data point i belongs to, for property t . Plugging update ( 12 ) gives, after some algebra,

Since \(x_{t i}\) is a unit vector, \( \exp (x_{t i}^T \log (y)) = x_{t i}^Ty\) for any vector \(y > 0\) , so

Finally, using ( 10 ) with \(\nu ^t\) gives

which is exactly the multiplicative update of the iterative proportional fitting algorithm, performed for property t .

B Expected values of BRFSS data

See Tables 1 , 2 , 3 , and 4 .

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Barratt, S., Angeris, G. & Boyd, S. Optimal representative sample weighting. Stat Comput 31 , 19 (2021). https://doi.org/10.1007/s11222-021-10001-1

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Weighted Decision Matrix: A Strategic Tool for Effective Decision-Making

In the realm of effective decision-making strategies, the Weighted Decision Matrix (WDM) stands out as a significant tool that supports individuals and organizations in making well-measured choices. Understanding how to prioritize and logically analyze diverse options is at the core of strategic problem-solving. Whether it's choosing the right supplier, selecting a new piece of technology, or deciding upon the direction of policy, a WDM serves as a methodical approach that can provide clarity and minimize the ambiguity that often surrounds complex decisions.

The exploration of the Weighted Decision Matrix here aims to dive into the nuances of using this tool strategically. With a focus on explaining its utility and offering recommendations for its effective use, this article hopes to provide readers with the essential insight that could be equivalent in depth and practicality to that provided by an online certificate course or a problem solving techniques course .

Definition of a Weighted Decision Matrix

The Weighted Decision Matrix, also known as a prioritization matrix or a decision grid, is a quantitative tool used to evaluate various alternatives against a set of criteria deemed important for the decision at hand. Essentially, this matrix is a tabular representation where rows often represent the alternatives, while the columns represent the various factors influencing the decision. Furthermore, each factor is assigned a weight reflecting its relative importance, which ensures that the decision matrix aligns with the priorities and values of the decision-maker.

Utility and Purpose of a Weighted Decision Matrix

The primary utility of a WDM is to provide a systematic and transparent method for decision making. It turns decision paralysis into action by converting qualitative judgments into quantifiable data. The purpose of a WDM is not only to reveal the most advantageous option but also to provide a record of the decision-making process. This aids in accountability and provides a valuable point of reference for future decisions.

Core Concepts of a Weighted Decision Matrix

Decision Factors: Explanation and Significance - The decision factors in a WDM play a crucial role as they are the dimensions or criteria against which each option will be ranked. Factors must be comprehensively identified and reflect all aspects of the decision scenario. Their significance lies in the fact that they act as benchmarks that facilitate the comparison of alternatives on a consistent basis. It is imperative that these factors are relevant and significant to the decision context to avoid misguidance in the overall analysis.

Weight Assignments: Importance in Decision Making - Weights are indicative of the relative importance of each decision factor. Assigning weights is a critical step which should be accomplished with strategic forethought. Weights usually sum to a total of 100% or a similar consistent value, representing the entire scope of the decision criteria. They act as multipliers, enhancing the effect of scores on the overall decision, which is especially important in distinguishing among factors that do not equally contribute to the final outcome.

Scoring System: Description and Application - Scores are attributed to each alternative for each decision factor. The scoring system often uses a numerical range, for example, 1 to 10, where higher numbers signify better alignment with the decision criteria. The application of scoring must be conducted judiciously, with adequate knowledge and unbiased judgment, to each option as these scores will ultimately be adjusted by the weights to provide a final comparative metric across all alternatives.

Calculation and Analysis: Understanding the Result - Calculation in a WDM is straightforward: multiply scores by their respective weights and sum them to get a total score for each option. The alternative with the highest total score is deemed to have the best overall alignment with the defined criteria and weights. This process allows for a varied, multifaceted set of options to be compared on a singular scale of preference or suitability.

Real-life Example to Illustrate Core Concepts - A real-life example of utilizing a WDM could be in the selection of a new software solution for a company—the decision factors might include cost, user-friendliness, compatibility with existing systems, and customer support. After determining the weights for these factors based on their relevance to the business’s needs, each software option would be scored against these factors. Analyzing the calculated scores would give a clear indication of which software aligns best with the company’s priorities.

Benefits and Limitations of Weighted Decision Matrix

Advantages of Using a Weighted Decision Matrix

Objective Decision Making: One of the primary benefits of using a WDM is the introduction of objectivity into the decision-making process. By relying on an established set of criteria and corresponding weights, subjective biases can be reduced, making the process more transparent and justifiable.

Prioritization of Choices: WDM's systematic approach allows decision-makers to prioritize options based on quantified data. This prioritization helps in the allocation of resources, be it time, money, or manpower, to the most advantageous alternatives.

Transparency and Clarity: When decisions are made using a WDM, the rationale becomes clear and transparent to all stakeholders involved, aiding in buy-in and reducing subsequent resistance to implementation.

Limitations of a Weighted Decision Matrix

Subjectivity of Weighing and Scoring: Despite its objective façade, WDM cannot completely eliminate subjectivity. The assignment of weights and scores can still reflect personal biases or misunderstandings of the decision context.

Dependence on Prior Knowledge and Experience: The effectiveness of a WDM is contingent upon the decision-maker’s understanding and experience. A lack of comprehensive knowledge about the alternatives or criteria could lead to skewed results.

Complexity for Large, Multifactorial Decisions: For decisions that involve a multitude of factors and options, a WDM can become unwieldy and complex. This might necessitate an initial round of simplification or filtering to make the matrix manageable.

Practical Examples of Benefits and Limitations

A WDM can be highly beneficial in assessing the environmental impact of different project proposals, providing a clear objective ranking based on various environmental criteria. Conversely, in complex scenarios like urban planning, where social, economic, environmental, and political factors intertwine, the limitations of a WDM become more pronounced, requiring a comprehensive approach that may go beyond the simplicity of a singular matrix.

Various Applications of the Weighted Decision Matrix

Use in Business Decision Making: In business, a WDM is employed in scenarios ranging from vendor selection to product feature prioritization or investment appraisals. It establishes an objective framework enabling managers to make decisions that are in line with strategic business objectives.

Application in Engineering and Technology: Engineers frequently use WDMs during the design phase of products or processes to evaluate different design alternatives or material choices, integrating both technical specifications and economic considerations into one evaluative process.

Role in Health and Medicine: In healthcare, decision matrices assist in policy formulation and clinical decision protocols, where patient outcomes, cost implications, and treatment efficacy must be weighed concurrently.

Significance in Environmental Policy Making: Environmental policy often requires a balance between development and conservation. A WDM aids in such decisions by quantifiably assessing the impact of various policy options against environmental criteria.

Practical Examples of Usage in Various Domains: A WDM can be applied to prioritize research and development projects, assess the cost-benefit of green technologies, or evaluate the risk management strategies of an organization. By doing so, it streamlines decision-making processes that could otherwise be overwhelmed by complexity and subjectivity.

Recommendations for Efficiently Using a Weighted Decision Matrix

Understanding Decision Context: An in-depth understanding of the context in which a decision is to be made is fundamental. This insight informs the relevancy of criteria and the appropriateness of the weighting, underlining the need for thorough research and consultation for each decision scenario.

Clear Definition of Factors and Weights: Defining factors and determining their weights are key steps that should be conducted with precision and consideration. Involving multiple stakeholders can aid in deriving a balanced view on which factors hold more significance over others.

Balanced Scoring System: The scoring system should be balanced and consistent. Each criterion must be assessed on the basis of reliable information, and scoring rules should be established upfront to avoid inconsistency during evaluation.

Comprehensive Review of Output: Following the calculation of scores, an introspective review of the output should be performed. This involves questioning the results, examining outliers, and ensuring that the conclusions drawn from the WDM make practical sense in the real world.

Recap of the Weighted Decision Matrix: The exploration of the Weighted Decision Matrix underscores its effectiveness as a strategic tool for decision-making. The facets ranging from defining criteria to assigning weights, applying a scoring system, and interpreting the results have been examined to illustrate a way forward for meticulous and informed choices.

Importance and Viability in Different Fields: The versatility of the WDM is apparent in its application across multiple disciplines—from business and technology to healthcare and environmental policy-making. Its utility in enhancing clarity, transparency, and objectivity in decisions is undeniable.

Encouragement for Appropriate and Effective Use: Implementing a WDM should be done with care, with a true understanding of its strengths and limitations. When used appropriately, it can greatly enhance the quality of decision-making processes and outcomes. It is encouraged that decision-makers in all fields consider its adoption where viable, perhaps even formalizing the skill set through targeted learning, such as attending online certificate courses focused on strategic decision making or problem solving techniques courses to deepen their mastery in this domain.

What are the essential components of a weighted decision matrix and how are they utilized in decision-making processes?

Weighted decision matrix fundamentals, understanding the matrix structure.

A weighted decision matrix stands as a tool. It aids in evaluating options. Users compare choices based on several criteria. These criteria have different levels of importance. Thus, they receive weights. The matrix design integrates both the weights and the criteria.

Criteria Selection and Weight Allocation

Deciding on criteria is crucial. These criteria should reflect critical decision factors. Examples include cost, efficiency, and sustainability. Weights show the significance of each criterion. They are usually numerical. The sum often equals 100 or 1, for easier comparison.

Scoring Each Option

Once criteria and weights are set, scoring begins. Options receive scores per criterion. Typically, this scoring uses a consistent scale. For instance, 1 to 5 or 1 to 10. Consistency in scoring is key. It ensures fair assessment across all options.

Multiplying Scores by Weights

Scores then multiply by corresponding weights. This step calculates a weighted score. It signifies an option's performance in a single criterion. High scores suggest a strong match between the option and the criterion.

Totaling Scores for Decision Making

Each option's weighted scores add up. The sum is its total weighted score. Comparison across options now becomes simpler. Decisions align with the highest total scores. They should reflect the most advantageous choices given the set criteria and weights.

Review for Informed Decision Making

A thorough review completes the process. Decision makers examine the matrix for insights. They seek a deeper understanding of each option's strengths and weaknesses. The matrix should guide but not dictate. Final decisions take into account the matrix alongside context and judgment.

Utilization in Decision-Making Processes

Prioritizing objectives.

The weighted decision matrix helps prioritize. Users define what matters most. They apply focus where it is due. Prioritization becomes systematic. It reduces the risk of overlooking key aspects.

Balancing Subjective and Objective Inputs

This tool balances the subjective with the objective. Numbers give form to preferences. They allow a structured comparison. Subjectivity exists in weight allocation. However, the overall process gains objectivity through numerical scoring.

Enhancing Transparency

The matrix provides transparency. Each step is explicit. Stakeholders follow the logic. They see why decisions emerge as they do. This transparency fosters trust and confidence in outcomes.

Facilitating Group Decision Making

Groups benefit from the matrix. It directs discussions. Scores and weights offer a common ground. Team members collaborate on criterion importance. They converge on perceptions of each option's value. It streamlines decision-making in group settings.

Supporting Consistency

A final key utilization is in ensuring consistency. Decisions across different scenarios maintain a standard approach. This consistency helps maintain strategic alignment. It builds reliability into the decision-making framework of organizations.

How does the concept of weighting in a decision matrix influence the overall outcome of strategic decisions?

Understanding weighting in decision matrices.

Weighting plays a pivotal role in decision matrices. It provides a systematic approach to evaluating options. Decision makers assign value to individual factors. These factors differ in importance. Weighting quantifies this variable importance.

The Mechanism of Weighting

Each criterion receives a weight. Weights reflect priority levels. Higher weights signify greater importance. These weights affect overall scores.

Breaking Down the Strategic Impact

Strategic decisions hinge on accurate assessments. Weighting modifies outcomes. It steers focus to critical areas. Informed decisions emerge from this focus.

- Factor identification is essential.

- Weights allocation follows.

- Strategic alignment guides weight distribution.

The Decision Making Process

The decision matrix streamlines complex choices. We begin with criteria listing. We associate weights with each criterion. Matrix completion involves option scoring.

Weighting: The Differentiator

Without weighting, all factors stand equal. This equality rarely matches real-world scenarios. Weighting introduces nuance. It acknowledges degrees of significance.

- Some factors trump others.

- Weighting respects this hierarchy.

- It adjusts scores accordingly.

The Outcome: A Tilted Balance

Weighted scoring tilts decision making. It favors options excelling in key areas. Weighting can decisively shift rankings. It often crowns a strategic choice.

Weighting and Subjectivity

Subjectivity can taint weighting. Strategic goals drive these subjective choices. Biases and perceptions influence them as well.

- Vigilance during weight assignment is crucial.

- Checks and balances reduce bias.

- Cross-functional team input can enhance objectivity.

Decision Matrix: A Tool for Prioritization

The decision matrix acts as a prioritization tool. It lays bare the foundation of strategic choices. Weighting transforms it into a sharp instrument.

- Priorities crystallize through weighting.

- Options compare against strategic aims.

- Clear winners often emerge from this process.

Final Thoughts on Weighting

Weighting’s influence extends far beyond mere numbers. It embodies an organization's strategic vision. It is integral to making informed decisions.

- Weighting enforces discipline.

- It demands rationality.

- It imposes strategic direction.

In essence, the weight applied in a decision matrix doesn't just influence the outcome. It often defines it.

Can you provide a specific example of an instance where a weighted decision matrix significantly improved the overall decision-making process in an organizational context?

The benefits of a weighted decision matrix.

A weighted decision matrix offers a structured method. It maximizes objectivity in decision-making. The tool assesses multiple options against diverse criteria. These criteria have varying levels of importance. The matrix amplifies the objectivity of the process. It aids in mitigating the impact of biases.

A Case in Organizational Decision-Making

Consider a corporation selecting new software. The decision was complex. Many stakeholders had to concur. A weighted decision matrix became instrumental. It defined key software decision criteria. Examples include cost, usability, and support. Each was assigned a weight reflecting its importance.

The team identified potential software options. Each option was then scored against the criteria. The scores were multiplied by the criteria weights. Aggregate scores yielded a quantitative assessment.

The Outcome

The matrix facilitated discussions. It brought clarity to contrasting views. Stakeholders explored their biases. They considered their preferences objectively. The matrix offered a transparent comparison. It was between functionality and Total Cost of Ownership (TCO). A consensus emerged. The best software was not the cheapest. Rather, it was the one offering the highest combined value.

The decision matrix improved the process significantly. It allowed a multifaceted evaluation. Prioritization of criteria aligned the selection. It matched the organization's strategic goals. The visualization of decisions fostered alignment. Stakeholders found it easier to understand the rationale. Agreement was reached with less resistance. The deployment of the chosen software progressed smoothly.

Key Factors in Weighted Decision Matrices

Transparency is a core benefit of a weighted decision matrix. Stakeholders see the scoring process. They can challenge and contribute to it. Collaboration is enhanced. Diverse opinions coalesce into a structured framework.

Accountability in decisions increases. Scores and weights are documented. They can serve for future retrospectives. The matrix avoids the "because I said so" scenario. Decisions are made with traceable logic. This cultivates trust and buy-in.

Flexibility is another strength of the matrix. It can adapt to changes in organizational priorities.

A weighted decision matrix can transform decision-making. It changes it from subjective to structured. It incorporates quantitative and qualitative evaluations. The decision-making process evolves. It becomes transparent, collaborative, and accountable. A matrix provides a functional roadmap. It guides organizations through complex decisions. The matrix aligns these decisions with strategic objectives.

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Relative Weights

Regression Analysis > Relative Weights

What are Relative Weights?

Johnson’s Relative Weights is a way quantify the relative importance of correlated predictor variables in regression analysis. “Relative importance” in this context means the proportion of the variance in y accounted for by x j . Put another way, it helps you figure out what variables contribute the most to r-squared.

Calculations

The actual calculations are complex and are usually performed with software. Chao et. al outline the general steps as:

• Transform the predictor variables into a set of orthogonal (uncorrelated) variables. These variables are “maximally related” to the predictor variables from an unweighted least squares perpective.
• Regress the dependent variables on the new set of transformed variables.

For most statistical software programs (like SPSS or JMP), run principal components regression to produce the orthogonal variables. Next, run least squares regression , using the results from the PCR to predict y-variables. The combined relative weights should add up to the initial r-squared.

Comparison to Other Indices

According to Chao et. al, Many relative importance of indices have been proposed over the years, including the Product Measure, General Dominance Index and Squared semipartial correlation. Johnson’s Relative Weights is superior in that it has better theoretical underpinnings and it always produces clear results even if the predictors have very high collinearity . Although Relative Weights and General Dominance Index (Shapley regression) produce similar results, Shapley’s method is computationally complex for more than a dozen or so variables and so is less often used. For example, a 30 variable relative weights model will run almost instantaneously on a home computer, while a 30 variable Shapley’s regression could take days .

References Chao, Yi-Chun. Quantifying the Relative Importance of Predictors in Multiple Linear Regression Analyses for Public Health Studies . Journal of Occupational & Environmental Hygiene Volume: 5 Issue 8 (2007) ISSN: 1545-9624

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• Reimbursement FAQs

APC (Ambulatory Payment Classifications) FAQ

1. what are apcs.

APCs, or "Ambulatory Payment Classifications," are the government's method of paying facilities for outpatient services for the Medicare program. The Federal Balanced Budget Act of 1997 required CMS to create a Medicare "Outpatient Prospective Payment System" (OPPS) for hospital outpatient services -analogous to the Medicare prospective payment system for hospital inpatients known as "Diagnosis Related Groups" or DRGs. This OPPS was implemented on August 1, 2000. APCs are an outpatient prospective payment system applicable only to hospitals and have no impact on physician payments under the Medicare Physician Fee Schedule. APC payments are made only to hospitals when the Medicare outpatient is discharged from the ED or clinic or transferred to another hospital (or other facility) not affiliated with the initial hospital where the patient received outpatient services. If the patient is admitted from a hospital clinic or ED, then there is no APC payment, and Medicare will pay the hospital under the inpatient DRG methodology.

2. How do APCs work?

Each APC comprises services similar in clinical intensity, resource utilization and cost. All services (identified by submission of CMS' Healthcare Common Procedure Coding System (HCPCS) codes on the hospital's UB 04 claim form) which are grouped under a specific APC result in an annually updated Medicare "prospective payment" for that particular APC. (Many HCPCS codes are derived directly from the AMA CPT.)  Since this payment is a prospective and "fixed" payment to the hospital, the hospital is at risk for potential "profit or loss" with each APC payment it receives. The payments are calculated by multiplying the APC relative weight by the OPPS conversion factor, and then there is a minor adjustment for geographic location. The payment is divided into Medicare's portion and patient co-pay. Co-pays are typically 20% of the APC payment rate.  A status indicator is assigned to each code to identify how the service is priced for payment.  For example, Status Indicator (SI) “F” - Corneal Tissue Acquisition; Certain CRNA Services and Hepatitis B Vaccines, is not paid under OPPS but is paid on a reasonable cost basis.

3. Why did CMS create APCs?

APCs were created to transfer some financial risks for providing outpatient services from the Federal government to individual hospitals, thereby achieving potential cost-savings for the Medicare program. By transferring financial risk to hospitals, APCs incentivize hospitals to provide outpatient services economically, efficiently, and profitably.

4. What areas of hospital outpatient services are paid under the APC methodology?

APC payments apply to outpatient surgery, outpatient clinics, emergency department, and observation services. APC payments also apply to outpatient testing (such as radiology and nuclear medicine imaging) and therapies (such as certain drugs, intravenous infusion therapies, and blood products).

Rural Emergency Hospitals: New Medicare Provider Type

There has been a growing concern that closures of rural hospitals and critical access hospitals (CAHs) are leading to a lack of services for people living in rural areas. Section 125 of the Consolidated Appropriations Act, 2021 (CAA) established a new Medicare provider type called Rural Emergency Hospitals (REHs), effective January 1, 2023. The REH designation is designed to maintain access to critical outpatient hospital services in communities that may not be able to support or sustain a Critical Access Hospital or small rural hospital. It provides a supplemental payment to hospitals for certain services covered by APCs. For information on the establishment of this new Medicare provider type, view the Rural Emergency Hospital fact sheet.

5. Are there hospital outpatient services which are NOT paid under APCs?

Yes, but bundling services into one payment remains an overarching theme. Durable Medical Equipment is paid for through non-APC methodology. However, most of the lab tests we order in the ED will now be bundled. Tests that are not bundled include diagnostic radiology studies, bedside ultrasounds, and EKGs. Add-ons that are not bundled include IV infusions and IV push dose medications. The OPPS bundles a lot of additional services, such as minor ancillary services with a geometric mean cost of less than or equal to $100 and assigned Status Indicator Q1 (Paid under OPPS); Addendum B displays APC assignments when services are separately payable:

• Packaged APC payment if billed on the same claim as an HCPCS code assigned status indicator “S,” “T,” or “V.”
• Composite APC payment if billed with specific combinations of services based on OPPS composite-specific payment criteria. Payment is packaged into a single payment for specific combinations of services.
• In other circumstances, payment is made through a separate APC payment. These include clinical laboratory services provided with other outpatient services, many add-on codes, and new device-intensive comprehensive APCs. These ancillaries will be paid separately when they are the only service provided, e.g., X-rays, EKGs, laboratory, blood bank and pathology services and specific respiratory tests and treatments.

6. Are drugs and supplies paid for under APCs?

Most drugs and supplies have costs included in the payment for specific visit levels or procedure APCs. This generally applies to drugs and supplies that have small associated cost. Drug administration services such as IVs and IM injections are paid for separately. More expensive medications, such as chemotherapy, may also be paid separately.

7. Which APCs apply to emergency department (ED) visits, and in 2024, what will the "average" US hospital receive in payment for these ED APCs?

There are hundreds of HCPCS (Healthcare Common Procedure Coding System) codes pertinent to the ED, payable under various APCs. The most common are the Evaluation and Management APCs.

Other common APCs in the ED

8. How are APC payments calculated?

APC payments are determined by multiplying an annually updated "relative weight" for a given service by an annually updated "Conversion Factor." The APC "conversion factor" for 2024 is $87.382. CMS publishes the annual updates to "relative weights" and the "conversion factor" in the November "Federal Register."

For example, to calculate the APC payment for APC 5051 (includes I & D of simple abscess—CPT 10060):

Relative Weight for APC 5051 =2.1851, the Conversion Factor for 2024 = $87.382. Multiply RW 2.1851 x CF $87.382 = $190.94 payment for APC 5051 for 2024 (for the "average US hospital").

The APC payment is modified according to adjustments for "Local Wage Indices." Medicare determined that 60% of the APC payment is due to employee wage costs. Since different areas of the country have widely divergent local wage scales, 60% of each APC payment is adjusted according to specific geographic locality.

The 2024 OPPS final rule increases reimbursement under the Medicare program by 3.1% for hospitals that meet quality reporting requirements. 

9. How do hospitals determine which Evaluation and Management service levels to assign for ED and clinic services as they relate to APCs and other payment methodologies?

For 2024, Medicare has not published "national standards" for hospital assignment of E/M code levels for outpatient services in clinics and the ED. CMS did, however, in 2014 collapse clinic, outpatient and office visit service levels into one payment, combining new and established patient visits into one payment. Emergency medicine remained exempt from the collapse of the E/M levels for 2024.

CMS has stated that each hospital may utilize its unique system to assign E/M levels, provided that the services are medically necessary, the coding methodology is accurate, consistently reproducible, and correlates with institutional resources utilized to provide a given level of service.  CMS continued to monitor the E/M levels coded nationally and indicated that the 2010 claims data used for the 2013 review indicates the normal and relatively stable distribution of clinic and emergency department visit levels compared to 2009 data.  CMS has noted a slight shift toward higher numbers of level 4 and 5 visits relative to lower level visits for Type A emergency department visit levels as patient acuity, complexity, and facility resource use of diagnostics has increased. 

In 2007, CMS established a lower level of ED called a Type B ED for services offered in a facility-based ED that was not open 24/7. See the November 27, 2007, Federal Registe r for further discussion on Type A and B EDs.

While there are no specific CMS national guidelines, CMS has given providers direction in the form of general guidelines, including the following:

• The coding guidelines should follow the intent of the associated CPT code descriptor, in that the guidelines should be designed to reasonably relate the intensity of hospital resources to the different levels of effort represented by the code.
• The coding guidelines should be based on hospital facility resources. The guidelines should not be based on physician resources.
• The coding guidelines should be clear to facilitate accurate payments and be usable for compliance purposes and audits.
• The coding guidelines should meet the HIPAA requirements.
• The coding guidelines should only require documentation that is clinically necessary for patient care.
• The coding guidelines should not facilitate upcoding or gaming.
• The coding guidelines should be written.
• The coding guidelines should be applied consistently across patients in the clinic or emergency department to which they apply.
• The coding guidelines should not change with great frequency.
• The coding guidelines should be readily available for fiscal intermediary (or, if applicable, MAC) review.
• The coding guidelines should result in coding decisions that could be verified.

10. Is there a requirement that the HCPCS codes submitted for payment to Medicare by the hospital and by a treating physician in the ED be identical, or "match?"

No. CMS has stated that Medicare does not expect a "high degree of correlation" between the HCPCS codes submitted by hospitals vs. those submitted by physicians. The AMA developed CPT codes to capture physician cognitive and procedural services and were never intended for capturing the utilization of hospital resources; Medicare recognizes there may be significant differences in coding between the hospitals and physicians even though the patient received services from both entities during the same outpatient encounter. Consider this scenario; the ED resources include the support of the ED physician and any consultant who comes to the emergency department.  As the facility HCPCS reflects the support and assistance provided to both physicians, you could expect to see a higher level of care for the facility than for the emergency physician.  Conversely, the physician’s level of service may exceed the E/M coded by the facility. The key concept is that facility and professional coding and billing are two distinct systems.

11. Can hospitals bill Medicare for the lowest level ED visit for patients who check into the ED and are "triaged" through a limited evaluation by a nurse but leave the ED before seeing a physician?

In 2011 OPPS, CMS restated its position on "Triage-only" visits, confirming that it does not specify the type of staff who may provide services. "A hospital may bill a visit code based on the hospital's own coding guidelines, which must reasonably relate the intensity of hospital resources to different levels of HCPCS codes. Services furnished must be medically necessary and documented."

However, in 2012 CMS indicated in a Facility FAQ that Hospital outpatient therapeutic services and supplies (including visits) must be furnished incident to a physician's service and under the order of a physician or other qualified practitioner. CMS stated that an ED visit would not be paid if the patient encounter did not meet the incident to requirement (the patient would need to be seen by an ED physician or QHP). Services provided by a nurse in response to a standing order do not satisfy this requirement. Since diagnostic services do not need to meet the requirements for incident to services, they may be coded even if the patient were to leave without being seen by the physician.

12. Do ICD-10-CM (Diagnosis codes) play a role in APC payments?

No, ICD-10-CM codes do not determine ED facility reimbursement, and since 2007, they are no longer required for observation coding. ICD-10-CM codes can establish medical necessity for the level of services or procedures billed, and Medicare's edit system thus looks for specific ICD-10-CM codes for some services. For each procedure, these ICD-10s can be identified by looking up CMS's local and national coverage decision (LCDs and NCDs) documents. 

13. How have APCs affected hospital outpatient coding?

Before Aug. 1, 2000, Medicare reimbursed hospitals for outpatient services on a "cost-basis." CPT codes were not required on the UB-92 claim forms, and hospitals received reimbursement based on their reported "costs" for drugs, supplies, E&M services (such as ED visits), etc.

Under OPPS, it is essential to document and capture all services provided by the hospital, since its efficiency and resource utilization will determine whether the hospital incurs a "profit or loss" on each Medicare outpatient encounter. Thus, it is imperative that hospital staff wholly and accurately document all services provided to Medicare beneficiaries in the outpatient areas.

Physicians can significantly assist their hospitals by being as diligent as possible in their documentation. For example, physician documentation of such services as insertion of a central venous line (CPT 36556 (APC 5183) and 36557 (APC 5184) will assist the hospital coders in the assignment of these codes—with ultimate payment in 2024 by Medicare of 5183 $3,040.18) to the "average US hospital"). Increasing cooperation between physicians and hospitals in medical records documentation is critical to the economic survival of both members of the healthcare team.

14. How do hospitals report procedures when billing an E/M level?

Evaluation and Management Services and other procedures are distinct and separately billable services. By billing a surgical procedure code that describes the service, the facility is paid for the resources used to support the performance of the procedure. Facility charges include support for all providers; emergency physician, mid-level provider or consultant who provide services in the emergency department for a patient.

Most supplies and medications associated with the procedure will be paid as a combined payment for the surgical service. The E/M service is billed separately and includes the services related to the Evaluation and Management service. It is permissible for hospitals to reference surgical procedures in their E/M criteria as a proxy for the acuity and resources for the Evaluation and Management services prior to and following the procedure. In the 2008 OPPS final rule, CMS clarified, “In the absence of national visit guidelines, hospitals have the flexibility to determine whether to include separately payable services as a proxy to measure hospital resource use that is not associated with those separately payable services.” The 2011 ED Facility coding guidelines include interventions and procedures that may serve as a proxy for the level of service provided.

15. How does billing for critical care under APCs differ from the critical care service billed by the physician?

Although CMS instructs hospitals to follow the content of the CPT Critical Care descriptors, there is one significant difference when billing facility Critical Care services. Physician billing of Critical Care time allows the counting of non-face-to-face time spent working on the patient’s behalf; APC facility billing does not.  All time billed for Critical Care by hospitals under APCs must account for patient face-to-face time, and cannot duplicate time spent by more than one individual simultaneously at the bedside. Thus, hospitals need to be aware that Critical Care time for the facility is counted differently than physician time and should address separate documentation of this service.

16. What is a Comprehensive APC?

CMS defines a comprehensive APC as a classification for providing a primary service and all adjunct services provided to support the delivery of the primary service. The comprehensive APC would treat most individually reported codes as components of the comprehensive service, resulting in a single prospective payment based on the cost of all individually reported codes on the claim representing the delivery of a primary service and all adjunct services provided to support that delivery.

CMS defines “adjunctive services” as any service that is integral, ancillary, supportive, and/or dependent to the primary service. These services are assigned Status Indicator J1. For example, HCPCS Code 93618, Heart rhythm pacing, assigned Status Indicator J1 as a Comprehensive APC under APC 5211, has a 2024 relative weight of 12.9904 for a total payment of $1,135.13. Thus, the APC payment for heart rhythm pacing would include any additional service associated with the pacing in the payment for the pacing service. As defined by Status Indicator J1, all covered Part B services on the claim are packaged with the primary “J1” service except for services with OPPS status indicators F, G, H, L and U, as well as ambulance services, diagnostic and screening mammography, and all preventive services.

Updated January 2024

The American College of Emergency Physicians (ACEP) has developed the Reimbursement & Coding FAQs and Pearls for informational purposes only.   The FAQs and Pearls have been developed by sources knowledgeable in their fields, reviewed by a committee, and are intended to describe current coding practice. However, ACEP cannot guarantee that the information contained in the FAQs and Pearls is in every respect accurate, complete, or up to date. The FAQs and Pearls are provided "as is" without warranty of any kind, either express or implied, including but not limited to the implied warranties of merchantability and fitness for a particular purpose. Payment policies can vary from payer to payer. ACEP, its committee members, authors, or editors assume no responsibility for, and expressly disclaim liability for, damages of any kind arising out of or relating to any use, non-use, interpretation of, or reliance on information contained or not contained in the FAQs and Pearls. In no event shall ACEP be liable for direct, indirect, special, incidental, or consequential damages arising from the use of such information or material. Specific coding or payment-related issues should be directed to the payer. For information about this FAQ/Pearl, or to provide feedback, please contact Jessica Adams, ACEP Reimbursement Director, at (469) 499-0222 or [email protected] . 

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Tips for using Johnson's relative weights analysis

What is Johnson's relative weights analysis? In this article, Michael Lieberman explains Johnson’s relative weights analysis, a technique used to evaluate how the response (dependent) variable relates to a set of predictors when those are correlated to each other.

Understanding relative importance weights

Editor's note: Michael Lieberman is the founder and president of Multivariate Solutions, a New York-based research consulting firm. He can be reached at [email protected].

I like to say that there is nothing new under the sun statistically speaking. Almost all the math in common multivariate analyses were proven more than a century ago. Most new products are a mélange of existing techniques with a simple twist.

Every so often, however, a new technique emerges that leverages prevalent methodologies with growing bandwidth of personal and cloud computing. In this piece I will introduce importance weighting, a useful technique in marketing that allows marketers to assign varying levels of importance or priority to different factors or elements within their marketing strategies. I will outline one that is gaining popularity in the marketing research world – Johnson’s relative weights analysis.

In 2000, Jeff Johnson wrote  a technical paper introducing relative importance weights. Prior to that, researchers relied on traditional statistics (e.g., correlations, standardized regression weights) that are known to yield affected information concerning variable importance – especially when predictor variables are highly correlated with one another. In the context of market research, relative weights refer to the importance or influence of different attributes or factors on consumer preferences or purchasing decisions. Common uses for relative weights are:

• Target audience segmentation.
• Marketing mix models.
• Advertising campaigns.
• Content marketing.
• Customer journey-mapping.
• Product line optimization.
• Brand equity measurement.

In Johnson’s relative weights analysis, the focus is on determining the relative impact of each variable on the dependent variable, taking into account the influence of other variables in the model. The relative weights of the variables are calculated based on their unique contribution to the outcome variable while considering the presence of other variables in the model.

The Johnson method utilizes not only standardized outcomes from regression analysis, but also correlations between the dependent and predictor variables, as well as eigenvector analysis (a linear algebra matrix method) into a more nuanced nine-step technique.

Ease of relative weighting

Johnson’s relative weighting can get granular with a large number of variables. By contrast, linear regression cannot easily handle, say, 20 variables. Differences between highly correlated variables would blur the outcome.

This is not the case with relative importance weights. Moreover, given the ease of programming, one can run the analysis across many dependent variables simply by changing one or two lines in R stat code. Using R or Python and calculating the relative importance weights has turned a multistep process into a few lines of code. The three lines of R stat code (below) reads in data and performs a relative weight analysis on a dependent variable and, in this case, nine predictor variables.

# Load the 'AvWeight' dataset

data(AvWeight)

# Fit a linear regression model

# Perform the relative weights analysis

rel_weights

The three lines, with slight changes of dependent variable in the code, produce Table 1, yielding a well-rounded and easy-to-replicate brand picture across nine attributes and six dependent variables. Darker shades of gray indicate a stronger relative weight.

Table 1 shows the output for our software client, ByteSmith Technologies.

Here are salient points I would report to ByteSmith at first glance:

• Net Promotor Score, overall company rating and consulting likelihood have no dominant drivers among the attributes.
• ByteSmith’s record on environmental programs is driven by “provides training for digital skills.” This could be a key finding for the company. A media campaign highlighting ByteSmith’s free community training may be preferable to a major cash donation to an environmental cause.
• “Expand product assortments through alliance partners,” which is a key variable as ByteSmith provides distribution of its computer services through partnership agreements, is driven by two attributes, “has a positive impact on economic opportunity” and “place a premium on service.”

Understanding relative weights in market research can assist businesses in product development, pricing strategies and marketing campaigns. It helps them identify key drivers of consumer preferences, prioritize product features and allocate resources effectively to meet customer demands.

Applying  Johnson's relative weight analysis  – Kano quadrant analysis

Let’s explore another example. Bourdain’s Barbecue wants to conduct a customer satisfaction survey to quantify customer loyalty and ascertain its market position vis-à-vis the increasingly competitive casual dining segment. In addition, they request that we conduct a Kano analysis to assess what sets them apart.

Kano analysis is, in essence, a measure of importance of the features to the customer and performance of the business. Often a standard importance question is asked in addition to performance ratings and a dependent variable, such as overall satisfaction or purchase intent. The top of the scale, whether 5, 7 or 10, is “very important,” and the bottom value is “not at all important.”

Kano's initial procedure is to determine inferred importance by testing the effect of variable performance measurements against a dependent variable. Here we are deploying Johnson’s relative weight analysis in place of standard importance regression analysis.

Alongside the relative importance weights score is the mean stated importance (Figure 1). These are the axes upon which a Kano analysis rests.

When graphed, with relative importance weights and stated importance centered and normalized, a Kano visual illustrates more clearly the Kano quadrants and how Bourdain’s Barbecue is perceived by its patrons.

Of course, many of the expected restaurant drivers do, in fact, place in the satisfiers key drivers’ quadrant in the upper right-hand corner – good food, fast service and good value for the money. It is the upper left-hand quadrant, though, that provides the most insight for Bourdain’s Barbecue and differentiates it from other casual dining steak establishments.

The Kano process employing Johnson relative weighting shows that three attributes help Bourdain’s Barbecue to stand out – two of them are not intuitive. Most barbecue steak casual dining restaurants do not offer “all you can eat” cornbread. In the competitive, “fill up the tank” culture of American dining, “all you can eat” is a powerful subliminal pleaser. Even though portions at Bourdain’s Barbecue are huge, patrons being able to eat all the cornbread they want is a distinguishing feature.

The second unexpected, distinguishing feature of the study finds that Bourdain’s supervised, themed play areas are a hit – and not just with the kids. In follow-up interviews, Bourdain’s Barbecue learned that parents find the area fun as well, and that they can place their children safely within sight at a supervised fun house. When the food arrives, they saunter over, scoop up the kids, feed them, then place them back in the fun house so that the parents can enjoy the remainder of their meal while the children rumble.

Attribute attrition – maximizing product lines

In our final example, I will mock-up a recent project. A regional supermarket chain, Lion Food Corporation, wants to create a slimmed-down version of its flagship stores, Cub. Lion Food Corporation has asked to perform a Johnson relative weighting with existing company data. The goal is to streamline product line offerings for a new concept, Cub Express. The company has uploaded a small subset of its sales data silo, 30 million records of shopping visits. Each line of the data represents the purchase from one visit to the flagship store.

There are a few key advantages to this kind of study:

• No field costs – Lion Foods has an enormous amount of data.
• Ease of data availability – Lion Foods provides the data in a form that allows the analyst to shape it into an R stat-ready data set.
• Flexibility – The model can be made to fit numerous analytics subsections if Lion Foods wants to regionalize Cub Express’s brand offerings, or if it would like to run multiple models for different products.

The first step is to organize the data set by individual shoppers. Each customer has a member number. Each line of data is one shopping trip, so individual shoppers may have 20-30 entries. We aggregate (summarize in a new data set by multiple visits for a single customer) so each individual has one row of data. We then create the following variables for analysis.

• Average amount spent per visit.
• Average number of visits over a finite period (e.g., six months).
• Bivariate variables listing each of the 24 breakfast cereals (1 = purchased, 0 = not purchased).

The cleaned data set of roughly 1 million customers is then uploaded into a data set to be opened in R stat for this specific analysis. The results are shown below (Table 2).

In Table 2, we see the Johnson relative weighting for each of the 24 breakfast cereal brands. Those in dark purple (left) are chosen to be stocked in new Cub Express stores. Those in light purple (right) will not be inventoried.

A more comprehensive approach to data analysis 

Johnson's relative weights have revolutionized the field of quantitative analysis and statistical modeling in the marketing research field. With its ability to capture nuanced relationships and incorporate varying importance levels, it offers a more comprehensive and accurate approach to data analysis. As we continue to explore the depths of data-driven insights, these weights serve as a powerful asset, paving the way for more sophisticated and informed decision-making processes.

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Methods for weighting decisions to assist modelers and decision analysts: a review of ratio assignment and approximate techniques.

1. Introduction

2. materials and methods.

• Objective and attribute structure. The structure of the objectives and the selection of weighting methods affect results and should be aligned to avoid bias;
• Attribute definitions affect weighting. The detail with which certain attributes are specified affects the weight assigned to them; that is, the division of an attribute can increase or decrease the weight of an attribute. For example, weighing price, service level, and distance separately as criteria for a mechanic selection led to different results than weighing shop characteristics (comprised of price and service level) and distance did [ 62 ];
• Number of attributes affects method choice. It is very difficult to directly or indirectly weight when one has to consider many attributes (e.g., double digits or more), owing to the greater difficulty associated with answering all the questions needed for developing attribute weights; Miller [ 63 ] advocates the use of five to nine attributes to avoid cognitive overburden;
• More attributes are not necessarily better. As the number of attributes increases, there is a tendency for the weights to equalize, meaning that it becomes harder to distinguish the difference between attributes in terms of importance as the number of significant attributes increases [ 64 ];
• Attribute dominance. If one attribute is weighted heavier than all other attributes combined, the correlation between the individual attribute score and the total preference score approaches one;
• Weights compared within but not among decision frameworks. The interpretation of an attribute weight within a particular modeling framework should be the same regardless of the method used to obtain weights [ 65 ]; however, the same consistency in attribute weighting cannot be said to be present across all multi-criteria decision analysis frameworks [ 66 ];
• Consider the ranges of attributes. People tend to neglect accounting for attribute ranges when assigning weights using weighting methods that do not stress them [ 56 , 67 ]; rather, these individuals seem to apply some intuitive interpretation of weights as a very generic degree of importance of attributes, as opposed to explicitly stating ranges, which is preferred [ 68 , 69 , 70 ]. This problem could occur when evaluating job opportunities. People may assume that salary is the most important factor, however, if the salary range is very narrow (e.g., a few hundred dollars), then other factors such as vacation days or available benefits may in fact be more important in the decision maker’s happiness.

3.1. Ratio Assignment Techniques

3.1.1. direct assignment technique (dat), dat step 1: assign points to each attribute, dat step 2: calculate weights, strengths of this approach, limitations of this approach, 3.1.2. simple multi attribute rating technique (smart), smart step 1: rank order attributes, smart step 2: establish the reference attribute, smart step 3: score attributes relative to the reference attribute, step 4: calculate weights, 3.1.3. swing weighting techniques (swing), swing step 1: rank order attributes, swing step 2: establish the reference attribute, swing step 3: score attributes relative to the reference attribute, swing step 4: calculate weights, 3.1.4. simple pairwise comparison, pairwise step 1: pairwise rank the attributes.

• Purchase Price vs. Attractiveness: Purchase Price Wins;
• Purchase Price vs. Reliability: Purchase Price Wins;
• Purchase Price vs. Gas Mileage: Purchase Price Wins;
• Purchase Price vs. Safety Rating: Purchase Price Wins;
• Attractiveness vs. Reliability: Reliability Wins;
• Attractiveness vs. Gas Mileage: Attractiveness Wins;
• Attractiveness vs. Safety Rating: Safety Wins;
• Reliability vs. Gas Mileage: Reliability Wins;
• Reliability vs. Safety Rating: Reliability Wins;
• Gas Mileage vs. Safety Rating: Safety Wins;

Pairwise Step 2: Calculate Weights

3.2. approximate techniques, 3.2.1. equal weighting technique, 3.2.2. rank ordered centroid (roc) technique, roc step 1: rank order attributes and establish rank indices, roc step 2: calculate the rank ordered centroid for each attribute, 3.2.3. rank summed weighting (rs) technique, rs step 1: rank order attributes and establish rank indices, rs step 2: calculate the rank summed weight for each attribute, 3.2.4. rank reciprocal weighting (rr) technique, rr step 1: rank order attributes and establish rank indices, rr step 2: calculate the rank summed weight for each attribute, 4. discussion, 4.1. characteristics of multi-criteria decision analysis techniques, 4.2. mcda as decision-making options for computational models, 4.2.1. mcda applicability for agent-based models, 4.2.2. mcda applicability for discrete event simulation models, 4.2.3. mcda applicability for system dynamics models, 4.3. limitations, 5. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Ezell, B.; Lynch, C.J.; Hester, P.T. Methods for Weighting Decisions to Assist Modelers and Decision Analysts: A Review of Ratio Assignment and Approximate Techniques. Appl. Sci. 2021 , 11 , 10397. https://doi.org/10.3390/app112110397

Ezell B, Lynch CJ, Hester PT. Methods for Weighting Decisions to Assist Modelers and Decision Analysts: A Review of Ratio Assignment and Approximate Techniques. Applied Sciences . 2021; 11(21):10397. https://doi.org/10.3390/app112110397

Ezell, Barry, Christopher J. Lynch, and Patrick T. Hester. 2021. "Methods for Weighting Decisions to Assist Modelers and Decision Analysts: A Review of Ratio Assignment and Approximate Techniques" Applied Sciences 11, no. 21: 10397. https://doi.org/10.3390/app112110397

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Relative Weighting

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Relative weighting is a prioritization approach that considers both the benefits of a feature and the cost of that feature. The technique is best applied for setting approximately quarterly goals rather than each sprint.

Start by selecting up to four prioritization criteria and enter these as columns on the table below. These are the factors that are most important to the organization over the period being prioritized. Good candidates for selection criteria include:

• Further the strategy
• Increase sales; maximize ROI
• Establish a competitive advantage (or eliminate one)
• Increase customer satisfaction
• Reduce cost
• Improve employee satisfaction
• Ensure regulatory compliance
• Support delivery of a strategic initiative
• Extend the brand
• Maximize value to partners
• Improve IT process and platform

Next, select the epics or themes to consider. Add these as rows in the relative weighting table on this page. Then in the cell at the intersection of each epic or theme and prioritization criterion, enter a value from 1 (low) to 9 (high) indicating the positive impact of that epic or theme on the prioritization criterion.

Finally, enter the development cost for each epic or theme in any unit you’d like. Normally this is done with story points arrived at from playing Planning Poker .

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